Climate change is a worldwide phenomenon that affects almost all aspects of life. The frequency and intensity of climatic hazards have increased globally, which disrupted agricultural livelihoods, water resources, food security, socioeconomic development and human health in many regions (Li et al., 2020a; Pour et al., 2020; Shiru et al., 2020; Wang et al., 2019). However, the impacts of climate change are not consistent throughout the globe. Arid and semi-arid regions are relatively more sensitive than other climate zones due to their vulnerable ecosystem (Ahmed et al., 2019c; Pour et al., 2020). Therefore, due to its arid to semi-arid environment, Central Asia is one of the most exposed areas to climate change (Yadav et al., 2019).
Climate change is generally perceived by studying precipitation and temperature variables (Sheffield and Wood, 2008). Global Climate Models (GCMs) are considered the most feasible tools for simulating the past climate and projection of future climate based on emission scenarios (Li et al., 2020a). Over the years, many GCMs have been developed, and the structure of Coupled Model Intercomparison Project (CMIPs) has been revised from CMIP1 to the newly designed CMIP6 (Meehl et al., 2014; Pascoe et al., 2019; Scher and Messori, 2019). CMIP6 is a set of GCMs and the latest released stage of the CMIP series (Eyring et al., 2016). In addition to the DECK (Diagnostic, Evaluation and Characterization of Klima experiments) and the CMIP6 historical replication, 21 individual MIPs have been endorsed by the CMIP panel for inclusion in CMIP6 (Eyring et al., 2015). The CMIP6 historical simulations consider both anthropogenic and geogenic forcing for the period 1850-2014 (Srivastava et al., 2020). The number of GCMs has increased concerning each generation of MIPs, reaching 55 in CMIP6 (Eyring et al., 2016). In comparison to CMIP5, CMIP6 has numerous advantages that combine RCPs with SSPs, improvements in the models and additional experiments, higher spatial resolution, less biases, and better representation of synoptic processes (Kamal et al., 2021; Su et al., 2021a).
Multi-model ensembles (MMEs) are usually used to minimize the GCMs uncertainty and generate outputs consistent with local (Ahmed et al., 2019a; Wang et al., 2018). Therefore, choosing a suitable set of GCM is important for better water resource management and climate change impact assessment, especially in developing nations with limited human and computational resources (Hassan et al., 2020; Lin and Tung, 2017). This is because GCMs are not free of biases, either underestimate or overestimate, although the uncertainty is smaller in CMIP6 GCMs than other CMIPs (Srivastava et al., 2020). Selected GCMs should replicate the observed climate in terms of spatial and temporal variability (Nashwan and Shahid, 2020). Generally, a preferable GCM ensemble is selected according to their past performance and the envelope method (Hassan et al., 2020; Salman et al., 2018). The past performance method examines GCMs capability to mimic the past climate without considering the future projection (Raju and Kumar, 2014; Srinivasa Raju and Nagesh Kumar, 2015). The envelope approach selects a set of GCMs models based on probable future climate projections without concerning a GCMs' performance to replicate the historical climate (Warszawski et al., 2014). However, the past performance method is most widely used for GCM ensemble member selection, considering that models able to replicate the observed climate of an area have a better ability to project its future climate (Khan et al., 2020; Nashwan and Shahid, 2020). In literature, different approaches have been offered for GCM selection and ensemble preparation (Hassan et al., 2020; Salman et al., 2018; Shiru et al., 2020).
Several studies assessed climate in central Asia (CA) at the basin, country or regional scale. Duulatov et al. (2019) used 4 GCMs of CMIP5 to assess rainfall changes over CA models for radiative concentration pathways (RCPs) 2.6 and 8.5. Gulakhmadov et al. (2020) used 5 GCMs of CMIP5 to project precipitation (Pr), maximum temperature (Tmx) and minimum temperature (Tmn) in the Vakhsh River Basin of CA for RCP4.5 and RCP8.5. Ta et al. (2018) assessed the performance of 37 GCMs of CMIP5 to mimic historical Pr over CA. Huang et al. (2014) evaluated the ability of 28 GCMs of CMIP5 to project changes in annual Pr over CA for RCPs 2.6 and 8.5. Xiong et al. (2021) assessed the skill of 24 CMIP5 models against climate research unit (CRU) historical temperature in CA. Zhao et al. (2018) used 25 CMIP5 to simulate the subtropical westerly jet and its impact on projected RCP8.5 summer rainfall over CA. Recently, CMIP6 GCMs are also used for climate projections in different parts of CA. Jiang et al. (2020) used 15 CMIP6 models to assess changes in Pr in CA for four shared socioeconomic pathways (SSPs). Li et al. (2020b) used 4 CMIP6 models to evaluate total column water vapor changes in the atmosphere over CA for different SSPs. Guo et al. (2021) assessed the ability of CMIP6 in simulating Pr over CA. Li et al. (2021) used four models of CMIP5 and CMIP6 to project future changes of biodiversity in CA for different RCPs and SSPs.
The Amu Darya River (ADR) is the primary supply of fresh water in CA, and it flows through five countries: Kyrgyzstan, Tajikistan, Afghanistan, Turkmenistan and Uzbekistan (Jalilov et al., 2016). The region's livelihood and national economy depend heavily on water supplies in the ADR basin (Saidmamatov et al., 2020). The basin is highly vulnerable to climate owing to its arid to semi-arid climate and fragile environment. However, only fewer studies evaluated climate change over the ADR basin. Hagg et al. (2013) used 6 CMIP3 GCMs to project annual and seasonal glacier changes and runoff in the upper ADR basin. White et al. (2014) assessed the ability of fourteen CMIP3 GCMs precipitation and temperature against CRU to project future water supply in the ADR basin. Lutz et al. (2013) evaluated CMIP3 and CMIP5 MME against The Asian Precipitation-Highly-Resolved Observational Data Integration Toward Evaluation (APHRODITE) and Princeton’s Global Meteorological Forcing Data (PGMFD) datasets to assess the impact of climate change on the future glacier extent in the ADR and Syr Darya river basins. Su et al. (2021b) developed an integrated multi-GCM Bayesian‐neural‐network hydrological study using GCM simulated climate to assess the influence of climate change on runoff in the ADR basin. Different studies in the basin and other CA regions used different sets of GCMs, which produced contradictory climate projections. This has also made decision-making based on the previous studies impossible. It is very important to recommend a suitable ensemble for climate projection in the ADR basin. Besides, reliable projection of basin's climate with the selected GCMs for SSPs is vital to aid climate mitigation policymaking.
This study aims to assess CMIP6 GCMs' ability to replicate observed climate in the ADR basin to recommend an appropriate ensemble for the basin's climate projections. Besides, the study projected the basin's climate with associated uncertainty using the selected GCMs to provide vital information required for climate change adaptation planning.